Please wait as your Twiminar loads.
#ESPTwiminars: Data Quality – Follow and respond to tweets with lessons learned/insights #ESPTwiminar3.1 http://t.co/IjWeyyZSOH
— ESP Solutions Group (@espsg) March 26, 2015
3rd in series of ESP Twitter Seminars. Participants will get insights into what works to ensure data quality from collection to reporting.
— ESP Solutions Group (@espsg) March 26, 2015
#ESPTwiminar3.2: Key to data quality is get data right from the start. Keeping them right is excellent data governance. #DataQuality
— ESP Solutions Group (@espsg) March 27, 2015
First sign of #DataQuality problems is agency spending resources & time cleaning data. Data should be clean when they enter the door.
— ESP Solutions Group (@espsg) March 27, 2015
#DataGovernance ensures the providers own the responsibility for initial #DataQuality and don’t pass that burden up to another agency.
— ESP Solutions Group (@espsg) March 27, 2015
Data Governance ensures that everyone along the way knows the rules and their own responsibility for data quality. #DataQuality
— ESP Solutions Group (@espsg) March 27, 2015
#ESPTwiminar3.3: Data quality is more than accuracy & reliability. Quality data yield info that’s valid for use & relied upon. #DataQuality
— ESP Solutions Group (@espsg) March 27, 2015
#DataQuality: Prime data quality objective for DBA & other IT pros is typically compliance with rules for formats, valid values.
— ESP Solutions Group (@espsg) March 27, 2015
Reliability is that the data are the same everywhere throughout the information system. “Same” doesn’t ensure quality. #DataQuality
— ESP Solutions Group (@espsg) March 27, 2015
Quality is that the data are the right data for the intended use—that’s data governance, again; & data are trusted & used. #DataQuality
— ESP Solutions Group (@espsg) March 27, 2015
#ESPTwiminar3.4: four great truths about data quality. First is data providers must know precisely what’s expected. #dataquality
— ESP Solutions Group (@espsg) March 30, 2015
Data Quality: A #Metadata dictionary is needed to establish and communicate definitions and valid codes to data providers. #dataquality
— ESP Solutions Group (@espsg) March 30, 2015
Data providers need to know they must attend to & comply with the #metadata standard, not submit “available” data. #dataquality
— ESP Solutions Group (@espsg) March 30, 2015
#ESPTwiminar3.5: Four great truths about data quality. Second is providers should use the data themselves. #dataquality
— ESP Solutions Group (@espsg) March 30, 2015
Data Quality: When the people who provide the data have a personal stake/use for the data, quality becomes a shared/vested interest.
— ESP Solutions Group (@espsg) March 30, 2015
When the data providers use the data they report, they will correct errors that impact their work. #dataquality
— ESP Solutions Group (@espsg) March 30, 2015
#ESPTwiminar3.6: Four great truths about data quality. Third is everyone, everywhere must check the data. #dataquality
— ESP Solutions Group (@espsg) March 31, 2015
Data Quality: A culture of ownership in checking data rather than moving data from one place to another is key. #dataquality
— ESP Solutions Group (@espsg) March 31, 2015
Everyone must be a data checker, not every-other person. Otherwise, the next person is the data cleaner for the prior one. #dataquality
— ESP Solutions Group (@espsg) March 31, 2015
#ESPTwiminar3.7: Four great truths about data quality. Fourth is, the data are readily available and dependably used. #dataquality
— ESP Solutions Group (@espsg) March 31, 2015
Quality data are those available to people & used by them. The more people looking at data, the more perspectives on quality get applied.
— ESP Solutions Group (@espsg) March 31, 2015
#ESPTwiminar3.8: Seven infrastructure components are required to support data quality. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
#DataQuality: Information Systems Architecture (metadata/HW/SW/governance) is the foundation on which all info systems are built/managed.
— ESP Solutions Group (@espsg) April 1, 2015
Infrastructure, the physical HW/SW/net/humans required to support tech systems must be adequate and supported. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Collections, the mechanisms for gathering data. These are submissions/reports from the data providers’ view. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Data Stores, the centralized locations where data are located, managed, and accessed; comprehensive data model included. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Decision Support System, the way data are provided to users for decision making (reports/queries/files/dashboards). #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Portal, system that authenticates/authorizes users to provide access & security to all information. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
User Support, system that trains/supports users to ensure efficient & proper use of the information. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
#ESPTwiminar3.9:6 observations about data quality from years of working with info system & data managers across the nation. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Accuracy-Tech staff focuses on accuracy, formats, data models-quality in, quality out means you get what they got. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Validity-Program staff say data must be consistent with & describe their actual program, a real measure of what they are doing. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Investment-The user of the data is the best reporter of the data. A consistent observation from many perspectives. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Certification-Someone should be responsible for certifying the quality of official data and statistics of an organization. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Publication-Public reporting is an important action in the evolution of an information system toward quality. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
Trust-Decision makers refer to the trust & confidence they must have in both the data and the individuals providing the data. #dataquality
— ESP Solutions Group (@espsg) April 1, 2015
#ESPTwiminar3.10:The Hierarchy of Data Quality provides a framework for rating your own data quality http://t.co/Y5VN3cOkqK #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
1. Data Defined. Invalid-bad data can be worse than no data at all. Metadata dictionary is a foundation for data quality. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
2. Data Available. Avoid inconsistent forms of measurement if using whatever data are handy. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
3. Official. Data combined, aggregated, analyzed, summarized become official, but may still have quality issues. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
4.Valid. Data governance has ensured the right data are collected the right way to match the questions/actions being informed. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
5. Quality. Data are valid for intended use; decision makers have confidence in & rely upon the data. Creates a continuous quality loop.
— ESP Solutions Group (@espsg) April 2, 2015
#ESPTwiminar3.11: 6 steps for ensuring data quality. This checklist incorporates the lessons learned across years & many agencies.
— ESP Solutions Group (@espsg) April 2, 2015
1. Are requirements known? Mandates, standards, policies, people skills must be known up front to guide all other steps. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
2. Is process well designed? Best practices for automation, interoperability combine w/ inclusion of all parties in processes. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
3. Is process well documented & communicated? Metadata dictionary, processes, training resources, calendar. Web access facilitates.
— ESP Solutions Group (@espsg) April 2, 2015
4.Is process well implemented? Checklists, audits, feedback reports. Trust but verify. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
5. Are data verified and compared? Business rules should check data for consistency, change, compliance, completeness. #dataquality
— ESP Solutions Group (@espsg) April 2, 2015
6. Are data appropriately analyzed & reported? FERPA. Technical reports document processes, analyses. Fair presentation within context.
— ESP Solutions Group (@espsg) April 2, 2015
#ESPTwiminar3.12: Quality data produce data-driven decisions made with confidence. Proven processes ensure quality. #dataquality
— ESP Solutions Group (@espsg) April 6, 2015
Steps for Ensuring #DataQuality & the Hierarchy of #DataQuality reflect lessons learned and proven processes. http://t.co/Y5VN3d5VPk
— ESP Solutions Group (@espsg) April 6, 2015
Go to http://t.co/indbytKCV3 to get the full white paper on #DataQuality and http://t.co/WTqzI1Df15 to print your badge for participation.
— ESP Solutions Group (@espsg) April 6, 2015
